{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vilassn/whisper_android/blob/master/models_and_scripts/whisper_tflite_model_generation_and_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AveoTQ563b3E"
      },
      "source": [
        "##Install TensorFlow, Tranformers and datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "3Dhj15CQ3ath",
        "outputId": "fbc4c8de-6be8-4802-8667-b273c145eb34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow==2.14.0\n",
            "  Downloading tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n",
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            "Collecting ml-dtypes==0.2.0 (from tensorflow==2.14.0)\n",
            "  Downloading ml_dtypes-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
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            "Collecting wrapt<1.15,>=1.11.0 (from tensorflow==2.14.0)\n",
            "  Downloading wrapt-1.14.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
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            "  Downloading tensorboard-2.14.1-py3-none-any.whl.metadata (1.7 kB)\n",
            "Collecting tensorflow-estimator<2.15,>=2.14.0 (from tensorflow==2.14.0)\n",
            "  Downloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl.metadata (1.3 kB)\n",
            "Collecting keras<2.15,>=2.14.0 (from tensorflow==2.14.0)\n",
            "  Downloading keras-2.14.0-py3-none-any.whl.metadata (2.4 kB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow==2.14.0) (3.2.2)\n",
            "Downloading tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (489.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m489.8/489.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ml_dtypes-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
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            "\u001b[?25hDownloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl (440 kB)\n",
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            "\u001b[?25hDownloading wrapt-1.14.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (77 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
            "Installing collected packages: wrapt, tensorflow-estimator, ml-dtypes, keras, google-auth-oauthlib, tensorboard, tensorflow\n",
            "  Attempting uninstall: wrapt\n",
            "    Found existing installation: wrapt 1.17.0\n",
            "    Uninstalling wrapt-1.17.0:\n",
            "      Successfully uninstalled wrapt-1.17.0\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.4.1\n",
            "    Uninstalling ml-dtypes-0.4.1:\n",
            "      Successfully uninstalled ml-dtypes-0.4.1\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 3.5.0\n",
            "    Uninstalling keras-3.5.0:\n",
            "      Successfully uninstalled keras-3.5.0\n",
            "  Attempting uninstall: google-auth-oauthlib\n",
            "    Found existing installation: google-auth-oauthlib 1.2.1\n",
            "    Uninstalling google-auth-oauthlib-1.2.1:\n",
            "      Successfully uninstalled google-auth-oauthlib-1.2.1\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.17.1\n",
            "    Uninstalling tensorboard-2.17.1:\n",
            "      Successfully uninstalled tensorboard-2.17.1\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.17.1\n",
            "    Uninstalling tensorflow-2.17.1:\n",
            "      Successfully uninstalled tensorflow-2.17.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorstore 0.1.71 requires ml_dtypes>=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.14.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed google-auth-oauthlib-1.0.0 keras-2.14.0 ml-dtypes-0.2.0 tensorboard-2.14.1 tensorflow-2.14.0 tensorflow-estimator-2.14.0 wrapt-1.14.1\n",
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            "Collecting datasets\n",
            "  Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
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            "Collecting xxhash (from datasets)\n",
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            "Collecting multiprocess<0.70.17 (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
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            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
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            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.14.0\n",
        "!pip install transformers\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0b2AJbu5CJ9Z"
      },
      "source": [
        "## Configure model to be generated as per requirement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUj7pZUWCJUI",
        "outputId": "383e29d4-bd95-4a78-8426-0b8993a7e70f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 50259], [2, 50359], [3, 50363]]\n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "import json\n",
        "\n",
        "######## Set the model as per requirement\n",
        "model_name = \"whisper-base\"          # whisper-tiny, whisper-tiny.en, whisper-base, whisper-base.en, whisper-small, whisper-small.en\n",
        "\n",
        "######## Set the language, task, and options as per requirement\n",
        "language_code = \"<|en|>\"             # <|en|>, <|fr|>, <|hi|>, <|ko|>, <|de|>, <|zh|>, <|ja|>, <|es|>, <|ar|>, <|ru|>, ...\n",
        "task_code     = \"<|transcribe|>\"     # <|transcribe|>, <|translate|>\n",
        "option_code   = \"<|notimestamps|>\"   # <|notimestamps|>, <|nocaptions|>\n",
        "\n",
        "# URL of the JSON file which stores the code mappings\n",
        "url = \"https://huggingface.co/openai/whisper-large/resolve/main/added_tokens.json\"\n",
        "\n",
        "# Send a GET request to download the file\n",
        "response = requests.get(url)\n",
        "\n",
        "# Check if the request was successful\n",
        "if response.status_code == 200:\n",
        "    # Parse the JSON content\n",
        "    code_mappings = response.json()\n",
        "else:\n",
        "    print(f\"Failed to download the file. Status code: {response.status_code}\")\n",
        "    code_mappings = {}\n",
        "\n",
        "# Construct forced_decoder_ids using the mappings\n",
        "forced_decoder_ids = [\n",
        "    [1, code_mappings[language_code]],\n",
        "    [2, code_mappings[task_code]],\n",
        "    [3, code_mappings[option_code]]\n",
        "]\n",
        "\n",
        "print(forced_decoder_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ng4Qq2rejVOE"
      },
      "source": [
        "##Import the libraries, load the model, do the inference"
      ]
    },
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        "collapsed": true,
        "id": "HKfmQ2kp627t",
        "outputId": "fb8acaa5-6c66-48fe-df4e-3a2e8b466e8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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          "metadata": {}
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        {
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFWhisperForConditionalGeneration.\n",
            "\n",
            "All the weights of TFWhisperForConditionalGeneration were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFWhisperForConditionalGeneration for predictions without further training.\n"
          ]
        },
        {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/73 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df233e02c62f4f9f86b8afc35b7ecf93"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[50258 50259 50359 50363  2221    13  2326   388   391   307   264 50244\n",
            "    295   264  2808  5359    11   293   321   366  5404   281  2928   702\n",
            "  14943    13 50257]], shape=(1, 27), dtype=int32)\n",
            "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.<|endoftext|>\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import transformers\n",
        "import datasets\n",
        "\n",
        "from datasets import load_dataset\n",
        "from transformers import WhisperProcessor, WhisperFeatureExtractor, TFWhisperForConditionalGeneration, WhisperTokenizer\n",
        "\n",
        "pretrained_model = f\"openai/{model_name}\"\n",
        "tflite_model_path = f\"{model_name}.tflite\"\n",
        "saved_model_dir = f\"tf_{model_name}_saved\"\n",
        "\n",
        "feature_extractor = WhisperFeatureExtractor.from_pretrained(pretrained_model)\n",
        "tokenizer = WhisperTokenizer.from_pretrained(pretrained_model, predict_timestamps=True)\n",
        "processor = WhisperProcessor(feature_extractor, tokenizer)\n",
        "model = TFWhisperForConditionalGeneration.from_pretrained(pretrained_model)\n",
        "\n",
        "# Loading dataset\n",
        "ds = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n",
        "inputs = feature_extractor(ds[0][\"audio\"][\"array\"], sampling_rate=ds[0][\"audio\"][\"sampling_rate\"], return_tensors=\"tf\")\n",
        "input_features = inputs.input_features\n",
        "\n",
        "# Generating Transcription\n",
        "generated_ids = model.generate(input_features=input_features)\n",
        "print(generated_ids)\n",
        "\n",
        "transcription = processor.tokenizer.decode(generated_ids[0])\n",
        "print(transcription)\n",
        "\n",
        "# Save the model\n",
        "# model.save(saved_model_dir) # not need to save here, saving using tf.saved_model.save() call"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EA65NFFIj4bN"
      },
      "source": [
        "## Prompt fix, patch to make forced_decoder_ids work"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "p1VOFBO9_MO-"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from transformers import TFForceTokensLogitsProcessor, TFLogitsProcessor\n",
        "from typing import List, Optional, Union, Any\n",
        "\n",
        "# Patching methods of class TFForceTokensLogitsProcessor(TFLogitsProcessor):\n",
        "\n",
        "def my__init__(self, force_token_map: List[List[int]]):\n",
        "    force_token_map = dict(force_token_map)\n",
        "    # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the\n",
        "    # index of the array corresponds to the index of the token to be forced, for XLA compatibility.\n",
        "    # Indexes without forced tokens will have an negative value.\n",
        "    force_token_array = np.ones((max(force_token_map.keys()) + 1), dtype=np.int32) * -1\n",
        "    for index, token in force_token_map.items():\n",
        "        if token is not None:\n",
        "            force_token_array[index] = token\n",
        "    self.force_token_array = tf.convert_to_tensor(force_token_array, dtype=tf.int32)\n",
        "\n",
        "def my__call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:\n",
        "    def _force_token(generation_idx):\n",
        "        batch_size = scores.shape[0]\n",
        "        current_token = self.force_token_array[generation_idx]\n",
        "\n",
        "        # Original code below generates NaN values when the model is exported to tflite\n",
        "        # it just needs to be a negative number so that the forced token's value of 0 is the largest\n",
        "        # so it will get chosen\n",
        "        #new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float(\"inf\")\n",
        "        new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float(1)\n",
        "        indices = tf.stack((tf.range(batch_size), tf.tile([current_token], [batch_size])), axis=1)\n",
        "        updates = tf.zeros((batch_size,), dtype=scores.dtype)\n",
        "        new_scores = tf.tensor_scatter_nd_update(new_scores, indices, updates)\n",
        "        return new_scores\n",
        "\n",
        "    scores = tf.cond(\n",
        "        tf.greater_equal(cur_len, tf.shape(self.force_token_array)[0]),\n",
        "        # If the current length is geq than the length of force_token_array, the processor does nothing.\n",
        "        lambda: tf.identity(scores),\n",
        "        # Otherwise, it may force a certain token.\n",
        "        lambda: tf.cond(\n",
        "            tf.greater_equal(self.force_token_array[cur_len], 0),\n",
        "            # Only valid (positive) tokens are forced\n",
        "            lambda: _force_token(cur_len),\n",
        "            # Otherwise, the processor does nothing.\n",
        "            lambda: scores,\n",
        "        ),\n",
        "    )\n",
        "    return scores\n",
        "\n",
        "TFForceTokensLogitsProcessor.__init__ = my__init__\n",
        "TFForceTokensLogitsProcessor.__call__ = my__call__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5c0ZSGWj9Gq"
      },
      "source": [
        "##Define a model with a serving signature and save it in TF SavedModel format.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "K968u9QS5jPL"
      },
      "outputs": [],
      "source": [
        "class GenerateModel(tf.Module):\n",
        "  def __init__(self, model):\n",
        "    super(GenerateModel, self).__init__()\n",
        "    self.model = model\n",
        "\n",
        "  @tf.function(\n",
        "    # shouldn't need static batch size, but throws exception without it (needs to be fixed)\n",
        "    input_signature=[\n",
        "      tf.TensorSpec((1, 80, 3000), tf.float32, name=\"input_features\"),\n",
        "    ],\n",
        "  )\n",
        "  def serving(self, input_features):\n",
        "    outputs = self.model.generate(\n",
        "      input_features,\n",
        "      # change below if you think your output will be bigger\n",
        "      # aka if you have bigger transcriptions\n",
        "      # you can make it 200 for example\n",
        "      max_new_tokens=448,\n",
        "      return_dict_in_generate=True,\n",
        "      forced_decoder_ids=forced_decoder_ids,\n",
        "    )\n",
        "    return {\"sequences\": outputs[\"sequences\"]}\n",
        "\n",
        "generate_model = GenerateModel(model=model)\n",
        "tf.saved_model.save(generate_model, saved_model_dir, signatures={\"serving_default\": generate_model.serving})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLfY_Fv4qHSC"
      },
      "source": [
        "## Convert the model from TF SavedModel format to TF lite"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "LBD7wMdQnsYc"
      },
      "outputs": [],
      "source": [
        "# Convert the model\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
        "converter.target_spec.supported_ops = [\n",
        "  tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\n",
        "  tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\n",
        "]\n",
        "\n",
        "# Learn about post training quantization\n",
        "# https://www.tensorflow.org/lite/performance/post_training_quantization\n",
        "\n",
        "# Dynamic range quantization which reduces the size of the model to 25%\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "\n",
        "# Float16 quantization reduces the size to 50%\n",
        "# converter.target_spec.supported_types = [tf.float16]\n",
        "\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model\n",
        "with open(tflite_model_path, 'wb') as f:\n",
        "    f.write(tflite_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jRRP3nQkEtC"
      },
      "source": [
        "##Test tflite model using TFLite Interpreter. Check transcription for dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3fbeAWE5ikD",
        "outputId": "e9974b3a-fad8-450a-a1bc-d13ad5427f29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>\n"
          ]
        }
      ],
      "source": [
        "# loaded model... now with generate!\n",
        "interpreter = tf.lite.Interpreter(tflite_model_path)\n",
        "\n",
        "tflite_generate = interpreter.get_signature_runner()\n",
        "generated_ids = tflite_generate(input_features=input_features)[\"sequences\"]\n",
        "# print(generated_ids)\n",
        "\n",
        "transcription = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n",
        "print(transcription)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOiytfUd4yyp"
      },
      "source": [
        "## Install faster-whisper for audio processing and testing model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "-k7BGLqM5Egh",
        "outputId": "c7861e6c-6f8e-4de8-d844-2b341b001dfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'faster-whisper'...\n",
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            "Receiving objects: 100% (1105/1105), 38.07 MiB | 25.15 MiB/s, done.\n",
            "Resolving deltas: 100% (697/697), done.\n",
            "Collecting faster-whisper\n",
            "  Downloading faster_whisper-1.1.1-py3-none-any.whl.metadata (16 kB)\n",
            "Collecting ctranslate2<5,>=4.0 (from faster-whisper)\n",
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            "\u001b[?25hInstalling collected packages: humanfriendly, ctranslate2, av, coloredlogs, onnxruntime, faster-whisper\n",
            "Successfully installed av-14.0.1 coloredlogs-15.0.1 ctranslate2-4.5.0 faster-whisper-1.1.1 humanfriendly-10.0 onnxruntime-1.20.1\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/SYSTRAN/faster-whisper.git\n",
        "!pip install faster-whisper"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xO1ZerhS4-in"
      },
      "source": [
        "## Test all audio files in loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Oc3YEK2j1FLb",
        "outputId": "be2d8363-b359-4414-fda9-c1bb81b97ee2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'audio_samples'...\n",
            "remote: Enumerating objects: 20, done.\u001b[K\n",
            "remote: Counting objects:   5% (1/20)\u001b[K\rremote: Counting objects:  10% (2/20)\u001b[K\rremote: Counting objects:  15% (3/20)\u001b[K\rremote: Counting objects:  20% (4/20)\u001b[K\rremote: Counting objects:  25% (5/20)\u001b[K\rremote: Counting objects:  30% (6/20)\u001b[K\rremote: Counting objects:  35% (7/20)\u001b[K\rremote: Counting objects:  40% (8/20)\u001b[K\rremote: Counting objects:  45% (9/20)\u001b[K\rremote: Counting objects:  50% (10/20)\u001b[K\rremote: Counting objects:  55% (11/20)\u001b[K\rremote: Counting objects:  60% (12/20)\u001b[K\rremote: Counting objects:  65% (13/20)\u001b[K\rremote: Counting objects:  70% (14/20)\u001b[K\rremote: Counting objects:  75% (15/20)\u001b[K\rremote: Counting objects:  80% (16/20)\u001b[K\rremote: Counting objects:  85% (17/20)\u001b[K\rremote: Counting objects:  90% (18/20)\u001b[K\rremote: Counting objects:  95% (19/20)\u001b[K\rremote: Counting objects: 100% (20/20)\u001b[K\rremote: Counting objects: 100% (20/20), done.\u001b[K\n",
            "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
            "remote: Total 20 (delta 0), reused 17 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (20/20), 5.79 MiB | 21.02 MiB/s, done.\n",
            "Iteration 1.......................................................\n",
            "\n",
            "Processing english_test1.wav...\n",
            " Four score and seven years ago, our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal.\n",
            "\n",
            "Processing jfk.wav...\n",
            " And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.\n",
            "\n",
            "Processing english_test2.wav...\n",
            " This is the micro-machine, representing the most miniature modicator of Michael machine. Each one has dramatic details for a fixed-ridden precision-page art plus incredible micro-machine pocket placettes. There's a police station, fire station, restaurant, service station, and more. Perfect pocket portables to take any place. And there are many miniature placettes to play with. Each one comes with its own special edition. Micro-machine vehicle and fun fantastic features that miraculously move. Raise the boat lift at the airport, Marina. Man, the gun turret at the army. It's clean your car at the car wash. Raise the toll bridge. And these placettes fit together to form a micro-machine world. Micro-machine pocket placettes are tremendous. They're tiny, so perfectly precise. So dazzling, we detail Joe on a pocket them all. Micro-machines are micro-machine pocket placettes sold separately from Galube. The smaller they are, the better they are.\n",
            "\n",
            "Processing MicInput.wav...\n",
            " Record your voice for transcription.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "from transformers import WhisperProcessor, WhisperFeatureExtractor\n",
        "from faster_whisper import decode_audio\n",
        "\n",
        "# Set up paths and model (whisper-tiny, whisper-tiny.en, whisper-base, whisper-base.en, whisper-small, whisper-small.en)\n",
        "# model_name = \"whisper-base.en\"\n",
        "# pretrained_model = f\"openai/{model_name}\"\n",
        "# tflite_model_path = f\"{model_name}.tflite\"\n",
        "\n",
        "######## NOTE: Specify the folder containing audio files\n",
        "!git clone https://github.com/vilassn/audio_samples.git\n",
        "audio_folder_path = 'audio_samples/en'\n",
        "#audio_folder_path = '/content/drive/MyDrive/Colab Notebooks/audio'\n",
        "\n",
        "feature_extractor = WhisperFeatureExtractor.from_pretrained(pretrained_model)\n",
        "tokenizer = WhisperTokenizer.from_pretrained(pretrained_model, predict_timestamps=True)\n",
        "processor = WhisperProcessor(feature_extractor, tokenizer)\n",
        "\n",
        "interpreter = tf.lite.Interpreter(tflite_model_path)\n",
        "tflite_generate = interpreter.get_signature_runner()\n",
        "\n",
        "# Number of iterations you want the loop to run\n",
        "iterations = 1000\n",
        "\n",
        "for i in range(1, iterations + 1):  # Start from 1 to print iteration number\n",
        "    print(f\"Iteration {i}.......................................................\\n\")  # Print iteration number and newline\n",
        "\n",
        "    # Loop through all files in the folder\n",
        "    for audio_file_name in os.listdir(audio_folder_path):\n",
        "        audio_file_path = os.path.join(audio_folder_path, audio_file_name)\n",
        "\n",
        "        if audio_file_name.endswith('.wav'):  # Process only .wav files\n",
        "            print(f\"Processing {audio_file_name}...\")\n",
        "\n",
        "            # Preprocess the audio file\n",
        "            input_audio = decode_audio(audio_file_path, sampling_rate=16000)\n",
        "            input_features = feature_extractor(input_audio, sampling_rate=16000, return_tensors=\"tf\").input_features\n",
        "\n",
        "            # Run the model\n",
        "            generated_ids = tflite_generate(input_features=input_features)[\"sequences\"]\n",
        "\n",
        "            # Decode and print transcription\n",
        "            transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "            print(f\"{transcription}\\n\")  # Add newline after each transcription"
      ]
    }
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